Projecting Grape Harvest Area and Production in Turkey Using Time Series Analysis

Yıl 2017, , 64 - 73, 29.12.2017

Sadiye Peral Eyduran Melekşen Akın

https://doi.org/10.13002/jafag4297

Öz

The objective of this study was to forecast grape harvest area and production in Turkey for 2016-2025 period. For this aim, the FAOSTAT data on grape harvest area and production of 1961-2015 period in Turkey was used. Exponential smoothing models were compared to model grape harvest area and production. Holt model results reflected that a decrease in grape harvest area from 453 985 ha to 382 250 ha was forecasted for the 2016-2025 period. According to the Holt model, the grape production forecasted as 3 819 753 tons in 2016 will increase to 3 944 376 tons in 2025. The projection results of this study could provide useful information for developing good policies for food sustainability, grape production and price structuring in Turkey for the next years.

Anahtar Kelimeler

Exponential smoothing, forecasting, grape, Holt method, time series

Kaynakça

• Badmus MA, Ariyo OS (2011). Forecasting cultivated areas and production of maize in nigerian using ARIMA model. Asian Journal of Agricultural Sciences, 3 (3), 171-176.
• Borkar P (2016). Modeling of groundnut production in India using ARIMA model. International Journal of Research in IT & Management. 6(3): 36-43.
• Celik S (2013). Modelling of production amount of nuts fruit by using Box-Jenkins technique. Yuzuncu Yil J. Agr. Sci. 23(1):18-30.
• Celik S, Karadas K, Eyduran E (2017). Forecasting groundnut production of Turkey via ARIMA models. The Journal of Animal and Plant Science.
• Eyduran SP, Akin M, Ercisli S, Eyduran E (2015). Phytochemical profiles and antioxidant activity of some grape accessions (Vitis spp.) native to eastern Anatolia of Turkey. J. Appl. Bot. Food Qual., 88, 5–9.
• FAOSTAT (2017). Statistical database of the food and agriculture organization of the United Nations. http://faostat.fao.org/.
• Hamjah MA (2014). Forecasting major fruit crops productions in Bangladesh using Box-Jenkins ARIMA model. Journal of Economics and Sustainable Development 5(7): 137-142.
• Karadas K, Celik S, Eyduran E, Hopoglu S (2017a). Forecasting production of some oil seed crops in Turkey using exponential smoothing methods. The Journal of Animal and Plant Science (in press).
• Karadas K, Celik S, Hopoglu S, Eyduran E, Iqbal F (2017b). A survey of the relationship between production amount, cultivation area and yield of cotton lint in Turkey using time series analysis. The Journal of Animal and Plant Science (in press).
• Kumar M, Anand M (2014). An application of time series ARIMA forecasting model for predicting sugarcane production in India. Studies in Business and Economics, 9(1), 81-94.
• Masuda T, Goldsmith PD (2009). World soybean production: area harvested, yield, and long-term projections. International Food and Agribusiness Management Association. 12(4): 143-161.
• Pektas A (2013). SPSS ile veri madenciligi. Dikeyeksen Yayın Dagitim, Yazilim ve Egitim Hizmetleri San. ve Tic. Ltd. Sti.; Istanbul.
• Semerci A, Ozer S (2011). Turkiye’de aycicegi ekim alanı, üretim miktarı ve verim değerinde olası değişimler. Journal of Tekirdag Agricultural Faculty, 8(3): 46-52.